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primeline
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Model Details
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primeline
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The model is intended to be used for German speech recognition tasks. It can be used as local transkription service or as a part of a larger pipeline for speech recognition tasks. While counting only half of the parameters of the large model, the quality is still very good and can be used for most tasks. The latency is low enough to be used in real-time applications when using optimization toolkits like tensorrt.
The dataset used for training is a filtered subset of the Common Voice dataset, multilingual librispeech and some internal data. The data was filtered and double checked for quality and correctness. We did some normalization to the text data, especially for casing and punctuation.
| Model | Parameters | link |
|---|---|---|
| Whisper large v3 german | 1.54B | link |
| Whisper large v3 turbo german | 809M | link |
| Distil-whisper large v3 german | 756M | link |
| tiny whisper | 37.8M | link |
The following hyperparameters were used during training:
python
import torchfrom transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipelinefrom datasets import load_datasetdevice = "cuda:0" if torch.cuda.is_available() else "cpu"torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32model_id = "primeline/distil-whisper-large-v3-german"model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)model.to(device)processor = AutoProcessor.from_pretrained(model_id)pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=30, batch_size=16, return_timestamps=True, torch_dtype=torch_dtype, device=device,)dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")sample = dataset[0]["audio"]result = pipe(sample)print(result["text"])
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Model author: Florian Zimmermeister
Disclaimer
text
This model is not a product of the primeLine Group. It represents research conducted by [Florian Zimmermeister](https://huggingface.co/flozi00), with computing power sponsored by primeLine. The model is published under this account by primeLine, but it is not a commercial product of primeLine Solutions GmbH. Please be aware that while we have tested and developed this model to the best of our abilities, errors may still occur. Use of this model is at your own risk. We do not accept liability for any incorrect outputs generated by this model.import torchfrom transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipelinefrom datasets import load_datasetdevice = "cuda:0" if torch.cuda.is_available() else "cpu"torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32model_id = "primeline/distil-whisper-large-v3-german"model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)model.to(device)processor = AutoProcessor.from_pretrained(model_id)pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=30, batch_size=16, return_timestamps=True, torch_dtype=torch_dtype, device=device,)dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")sample = dataset[0]["audio"]result = pipe(sample)print(result["text"])This model is not a product of the primeLine Group. It represents research conducted by [Florian Zimmermeister](https://huggingface.co/flozi00), with computing power sponsored by primeLine. The model is published under this account by primeLine, but it is not a commercial product of primeLine Solutions GmbH.Please be aware that while we have tested and developed this model to the best of our abilities, errors may still occur. Use of this model is at your own risk. We do not accept liability for any incorrect outputs generated by this model.